


import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


A			:						Tuple        =  get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')



@require_sentencepiece
@require_tokenizers
class 				A       (			UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					GPTSwaTokenizer
					A__     =					False
					A__     =					True
					A__     =					False

					def     lowerCamelCase__	(self       :		List[Any]						)		->  Optional[int]:




							"""simple docstring"""


							super().setUp()

							# We have a SentencePiece fixture for testing
							lowercase__	     =						GPTSwaTokenizer(_UpperCAmelCase ,   eos_token="""<unk>""" ,   bos_token="""<unk>""" ,   pad_token="""<unk>"""						)

							tokenizer.save_pretrained(self.tmpdirname						)

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		Any						)		->  int:




							"""simple docstring"""


							lowercase__	     =						"""This is a test"""
							lowercase__	     =						"""This is a test"""
							return input_text, output_text

					def     lowerCamelCase__	(self       :		int						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						"""<s>"""
							lowercase__	     =						1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase						) ,   _UpperCAmelCase						)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase						) ,   _UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						list(self.get_tokenizer().get_vocab().keys()						)

							self.assertEqual(vocab_keys[0] ,   """<unk>"""						)
							self.assertEqual(vocab_keys[1] ,   """<s>"""						)
							self.assertEqual(vocab_keys[-1] ,   """j"""						)
							self.assertEqual(len(_UpperCAmelCase						) ,   2000						)

					def     lowerCamelCase__	(self       :		Tuple						)		->  Dict:




							"""simple docstring"""


							self.assertEqual(self.get_tokenizer().vocab_size ,   2000						)

					def     lowerCamelCase__	(self       :		Dict						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						GPTSwaTokenizer(_UpperCAmelCase						)

							lowercase__	     =						tokenizer.tokenize("""This is a test"""						)
							self.assertListEqual(_UpperCAmelCase ,   ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]						)

							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						) ,   [465, 287, 265, 631, 842]						)

							lowercase__	     =						tokenizer.tokenize("""I was born in 92000, and this is falsé."""						)
							# fmt: off
							self.assertListEqual(
							    _UpperCAmelCase ,   ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""] ,   )
							# fmt: on

							lowercase__	     =						tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)
							self.assertListEqual(
							    _UpperCAmelCase ,   [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260] ,   )

							lowercase__	     =						tokenizer.convert_ids_to_tokens(_UpperCAmelCase						)
							# fmt: off
							self.assertListEqual(
							    _UpperCAmelCase ,   ["""▁I""", """▁was""", """▁bor""", """n""", """▁in""", """▁""", """<0x39>""", """2""", """0""", """0""", """0""", """,""", """▁and""", """▁this""", """▁is""", """▁f""", """al""", """s""", """<0xC3>""", """<0xA9>""", """."""]						)
							# fmt: on

					def     lowerCamelCase__	(self       :		Tuple						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						GPTSwaTokenizer(_UpperCAmelCase						)
							lowercase__	     =						["""This is a test""", """I was born in 92000, and this is falsé."""]
							lowercase__	     =						[
							    [465, 287, 265, 631, 842],
							    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
							]

							# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
							for text, expected_ids in zip(_UpperCAmelCase ,   _UpperCAmelCase						):
									self.assertListEqual(tokenizer.encode_fast(_UpperCAmelCase						) ,   _UpperCAmelCase						)

							# Test that decode_fast returns the input text
							for text, token_ids in zip(_UpperCAmelCase ,   _UpperCAmelCase						):
									self.assertEqual(tokenizer.decode_fast(_UpperCAmelCase						) ,   _UpperCAmelCase						)


					@slow
					def     lowerCamelCase__	(self       :		int						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						[
							    """<|python|>def fibonacci(n)\n    if n < 0:\n        print('Incorrect input')""",
							    """Hey there, how are you doing this fine day?""",
							    """This is a text with a trailing spaces followed by a dot     .""",
							    """Häj sväjs lillebrör! =)""",
							    """Det är inget fel på Mr. Cool""",
							]

							# fmt: off
							lowercase__	     =						{"""input_ids""": [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
							# fmt: on
							self.tokenizer_integration_test_util(
							    expected_encoding=_UpperCAmelCase ,   model_name="""AI-Sweden/gpt-sw3-126m""" ,   sequences=_UpperCAmelCase ,   )







'''simple docstring'''







import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD







def 						_A      ( ):






   '''simple docstring'''







   A__       =     'https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg'
   A__       =     Image.open(requests.get(UpperCAmelCase      ,stream=UpperCAmelCase						).raw						).convert('RGB'						)

   return image







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []
   # fmt: off

   # vision encoder
   rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding')						)
   rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding')						)
   rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight')						)
   rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias')						)
   rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight')						)
   rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias')						)

   for i in range(config.vision_config.num_hidden_layers						):
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.weight""", F"""vision_model.encoder.layers.{i}.layer_norm1.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.bias""", F"""vision_model.encoder.layers.{i}.layer_norm1.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.weight""", F"""vision_model.encoder.layers.{i}.layer_norm2.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.bias""", F"""vision_model.encoder.layers.{i}.layer_norm2.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.attn.qkv.weight""", F"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.weight""", F"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",)						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.bias""", F"""vision_model.encoder.layers.{i}.self_attn.projection.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc1.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc1.bias""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc2.weight""")						)
      rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc2.bias""")						)

   # QFormer
   rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.embeddings.layernorm.weight')						)
   rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.embeddings.layernorm.bias')						)

   # fmt: on
   return rename_keys







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     dct.pop(UpperCAmelCase						)
   A__       =     val







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   for i in range(config.vision_config.num_hidden_layers						):
      # read in original q and v biases
      A__       =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""						)
      A__       =     state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""						)

      # next, set bias in the state dict
      A__       =     torch.cat((q_bias, torch.zeros_like(UpperCAmelCase      ,requires_grad=UpperCAmelCase						), v_bias)						)
      A__       =     qkv_bias







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     364 if 'coco' in model_name else 224
   A__       =     InstructBlipVisionConfig(image_size=UpperCAmelCase						).to_dict()

   # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
   # seems like flan-T5 models don't have bos_token_id properly set?
   if "t5-xl" in model_name:
      A__       =     TaConfig.from_pretrained('google/flan-t5-xl'      ,dense_act_fn='gelu'      ,bos_token_id=1						).to_dict()
   elif "t5-xxl" in model_name:
      A__       =     TaConfig.from_pretrained('google/flan-t5-xxl'      ,dense_act_fn='gelu'      ,bos_token_id=1						).to_dict()
   elif "vicuna-7b" in model_name:
      A__       =     LlamaConfig.from_pretrained('decapoda-research/llama-7b-hf'      ,vocab_size=32001						).to_dict()
   elif "vicuna-13b" in model_name:
      A__       =     LlamaConfig.from_pretrained('decapoda-research/llama-13b-hf'      ,vocab_size=32001						).to_dict()
   else:
      raise ValueError('Model name not supported'						)

   # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
   A__       =     InstructBlipQFormerConfig(vocab_size=30523						).to_dict()
   A__       =     InstructBlipConfig(vision_config=UpperCAmelCase      ,text_config=UpperCAmelCase      ,qformer_config=UpperCAmelCase						)

   return config, image_size







@torch.no_grad()
def 						_A      ( UpperCAmelCase      ,UpperCAmelCase=None      ,UpperCAmelCase=False						):






   '''simple docstring'''







   A__       =     AutoTokenizer.from_pretrained('bert-base-uncased'      ,truncation_side='left'						)
   qformer_tokenizer.add_special_tokens({'bos_token': '[DEC]'}						)

   if "t5" in model_name:
      A__       =     TaTokenizerFast.from_pretrained('google/flan-t5-xl'      ,truncation_side='left'						)
   elif "vicuna" in model_name:
      # the following was used in the original implementation:
      # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
      # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
      # tokenizer.add_special_tokens({"bos_token": "</s>"})
      # tokenizer.add_special_tokens({"eos_token": "</s>"})
      # tokenizer.add_special_tokens({"unk_token": "</s>"})
      A__       =     LlamaTokenizerFast.from_pretrained(
          'huggyllama/llama-7b'      ,truncation_side='left'      ,bos_token='</s>'      ,unk_token='</s>'						)
      tokenizer.add_special_tokens({'pad_token': '[PAD]'}						)

   A__  ,			A__       =     get_blipa_config(UpperCAmelCase						)
   A__       =     InstructBlipForConditionalGeneration(UpperCAmelCase						).eval()

   A__       =     {
       'instructblip-vicuna-7b': ('blip2_vicuna_instruct', 'vicuna7b'),
       'instructblip-vicuna-13b': ('blip2_vicuna_instruct', 'vicuna13b'),
       'instructblip-flan-t5-xl': ('blip2_t5_instruct', 'flant5xl'),
       'instructblip-flan-t5-xxl': ('blip2_t5_instruct', 'flant5xxl'),
   }

   A__  ,			A__       =     model_name_to_original[model_name]

   # load original model
   print('Loading original model...'						)
   A__       =     'cuda:1' if torch.cuda.is_available() else 'cpu'
   A__       =     'cuda:2' if torch.cuda.is_available() else 'cpu'
   A__  ,			A__  ,			A__       =     load_model_and_preprocess(
       name=UpperCAmelCase      ,model_type=UpperCAmelCase      ,is_eval=UpperCAmelCase      ,device=UpperCAmelCase						)
   original_model.eval()
   print('Done!'						)

   # update state dict keys
   A__       =     original_model.state_dict()
   A__       =     create_rename_keys(UpperCAmelCase						)
   for src, dest in rename_keys:
      rename_key(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   # some keys can be renamed efficiently
   for key, val in state_dict.copy().items():
      A__       =     state_dict.pop(UpperCAmelCase						)
      if key.startswith('Qformer.bert'						):
         A__       =     key.replace('Qformer.bert'      ,'qformer'						)
      if "attention.self" in key:
         A__       =     key.replace('self'      ,'attention'						)
      if "llm_proj" in key:
         A__       =     key.replace('llm_proj'      ,'language_projection'						)
      if "t5_proj" in key:
         A__       =     key.replace('t5_proj'      ,'language_projection'						)
      if key.startswith('llm_model'						):
         A__       =     key.replace('llm_model'      ,'language_model'						)
      if key.startswith('t5'						):
         A__       =     key.replace('t5'      ,'language'						)
      A__       =     val

   # read in qv biases
   read_in_q_v_bias(UpperCAmelCase      ,UpperCAmelCase						)

   # note: weights get loaded in torch.float32 by default
   hf_model.load_state_dict(UpperCAmelCase      ,strict=UpperCAmelCase						)

   A__       =     load_demo_image()
   A__       =     'What is unusual about this image?'

   # create processor
   A__       =     BlipImageProcessor(
       size={'height': image_size, 'width': image_size}      ,image_mean=UpperCAmelCase      ,image_std=UpperCAmelCase						)
   A__       =     InstructBlipProcessor(
       image_processor=UpperCAmelCase      ,tokenizer=UpperCAmelCase      ,qformer_tokenizer=UpperCAmelCase      ,)
   A__       =     processor(images=UpperCAmelCase      ,text=UpperCAmelCase      ,return_tensors='pt'						).to(UpperCAmelCase						)

   # make sure processor creates exact same pixel values
   A__       =     vis_processors['eval'](UpperCAmelCase						).unsqueeze(0						).to(UpperCAmelCase						)
   A__       =     inputs.pixel_values
   assert torch.allclose(original_pixel_values.to(pixel_values.device						)      ,UpperCAmelCase						)

   original_model.to(UpperCAmelCase						)
   hf_model.to(UpperCAmelCase						)
   with torch.no_grad():
      if "vicuna" in model_name:
         A__       =     original_model({'image': original_pixel_values, 'text_input': [prompt]}						).logits
         A__       =     hf_model(**UpperCAmelCase						).logits
      else:
         A__       =     original_model(
             {'image': original_pixel_values, 'text_input': [prompt], 'text_output': ['\n']}						).logits
         A__       =     tokenizer('\n'      ,return_tensors='pt'						).input_ids.to(UpperCAmelCase						)
         A__       =     label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id      ,-100						)
         A__       =     hf_model(**UpperCAmelCase      ,labels=UpperCAmelCase						).logits

   print('First values of original logits:'      ,original_logits[0, :3, :3]						)
   print('First values of HF logits:'      ,logits[0, :3, :3]						)

   # assert values
   assert original_logits.shape == logits.shape
   A__       =     1e-4 if 'vicuna' in model_name else 1e-5
   assert torch.allclose(original_logits.to(logits.device						)      ,UpperCAmelCase      ,atol=UpperCAmelCase						)
   print('Looks ok!'						)

   print('Generating with original model...'						)
   A__       =     original_model.generate({'image': original_pixel_values, 'prompt': prompt}      ,num_beams=5						)

   # important: we need to cast the weights of the HF model to the appropriate type
   print('Generating with HF model...'						)
   A__       =     hf_model.generate(
       **UpperCAmelCase      ,do_sample=UpperCAmelCase      ,num_beams=5      ,max_length=256      ,min_length=1      ,top_p=0.9      ,repetition_penalty=1.5      ,length_penalty=1.0      ,temperature=1      ,)
   if "vicuna" in model_name:
      # convert output id 0 to 2 (eos_token_id)
      # TODO add this in the generate method?
      A__       =     2
   print('Original generation:'      ,UpperCAmelCase						)
   A__       =     processor.batch_decode(UpperCAmelCase      ,skip_special_tokens=UpperCAmelCase						)
   A__       =     [text.strip() for text in output_text]
   print('HF generation:'      ,UpperCAmelCase						)

   if pytorch_dump_folder_path is not None:
      processor.save_pretrained(UpperCAmelCase						)
      hf_model.save_pretrained(UpperCAmelCase						)

   if push_to_hub:
      processor.push_to_hub(F"""Salesforce/{model_name}"""						)
      hf_model.push_to_hub(F"""Salesforce/{model_name}"""						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser()
    lowerCAmelCase_     						=				[
        '''instructblip-vicuna-7b''',
        '''instructblip-vicuna-13b''',
        '''instructblip-flan-t5-xl''',
        '''instructblip-flan-t5-xxl''',
    ]
    parser.add_argument(
        '''--model_name''',
        default='''instructblip-flan-t5-xl''',
        choices=choices,
        type=str,
        help='''Path to hf config.json of model to convert''',
    )
    parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
    parser.add_argument(
        '''--push_to_hub''',
        action='''store_true''',
        help='''Whether to push the model and processor to the hub after converting''',
    )

    lowerCAmelCase_     						=				parser.parse_args()

    convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


